Maximum likelihood abundance estimation from capture‐recapture data when covariates are missing at random

Biometrics ◽  
2020 ◽  
Author(s):  
Yang Liu ◽  
Yukun Liu ◽  
Pengfei Li ◽  
Lin Zhu
Biometrics ◽  
2019 ◽  
Vol 75 (4) ◽  
pp. 1345-1355 ◽  
Author(s):  
Richard Glennie ◽  
David L. Borchers ◽  
Matthew Murchie ◽  
Bart J. Harmsen ◽  
Rebecca J. Foster

2019 ◽  
Vol 49 (21) ◽  
pp. 5273-5293
Author(s):  
George Lucas Moraes Pezzott ◽  
Luis Ernesto Bueno Salasar ◽  
José Galvão Leite ◽  
Francisco Louzada-Neto

Author(s):  
Tamar Gadrich ◽  
Guy Katriel

AbstractWe consider the problem of estimating the rate of defects (mean number of defects per item), given the counts of defects detected by two independent imperfect inspectors on one sample of items. In contrast with the setting for the well-known method of Capture–Recapture, we do not have information regarding the number of defects jointly detected by both inspectors. We solve this problem by constructing two types of estimators—a simple moment-type estimator, and a complicated maximum-likelihood (ML) estimator. The performance of these estimators is studied analytically and by means of simulations. It is shown that the ML estimator is superior to the moment-type estimator. A systematic comparison with the Capture–Recapture method is also made.


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